source: sasview/park_integration/ParkFitting.py @ fb8d4050

ESS_GUIESS_GUI_DocsESS_GUI_batch_fittingESS_GUI_bumps_abstractionESS_GUI_iss1116ESS_GUI_iss879ESS_GUI_iss959ESS_GUI_openclESS_GUI_orderingESS_GUI_sync_sascalccostrafo411magnetic_scattrelease-4.1.1release-4.1.2release-4.2.2release_4.0.1ticket-1009ticket-1094-headlessticket-1242-2d-resolutionticket-1243ticket-1249ticket885unittest-saveload
Last change on this file since fb8d4050 was a9e04aa, checked in by Gervaise Alina <gervyh@…>, 16 years ago

added fitting selection methods

  • Property mode set to 100644
File size: 5.2 KB
Line 
1"""
2    @organization: ParkFitting module contains SansParameter,Model,Data
3    FitArrange, ParkFit,Parameter classes.All listed classes work together to perform a
4    simple fit with park optimizer.
5"""
6import time
7import numpy
8import park
9from park import fit,fitresult
10from park import assembly
11from park.fitmc import FitSimplex, FitMC
12from sans.guitools.plottables import Data1D
13from Loader import Load
14from AbstractFitEngine import FitEngine,FitArrange,Model
15
16
17class ParkFit(FitEngine):
18    """
19        ParkFit performs the Fit.This class can be used as follow:
20        #Do the fit Park
21        create an engine: engine = ParkFit()
22        Use data must be of type plottable
23        Use a sans model
24       
25        Add data with a dictionnary of FitArrangeList where Uid is a key and data
26        is saved in FitArrange object.
27        engine.set_data(data,Uid)
28       
29        Set model parameter "M1"= model.name add {model.parameter.name:value}.
30        @note: Set_param() if used must always preceded set_model()
31             for the fit to be performed.
32        engine.set_param( model,"M1", {'A':2,'B':4})
33       
34        Add model with a dictionnary of FitArrangeList{} where Uid is a key and model
35        is save in FitArrange object.
36        engine.set_model(model,Uid)
37       
38        engine.fit return chisqr,[model.parameter 1,2,..],[[err1....][..err2...]]
39        chisqr1, out1, cov1=engine.fit({model.parameter.name:value},qmin,qmax)
40        @note: {model.parameter.name:value} is ignored in fit function since
41        the user should make sure to call set_param himself.
42    """
43    def __init__(self):
44        """
45            Creates a dictionary (self.fitArrangeList={})of FitArrange elements
46            with Uid as keys
47        """
48        self.fitArrangeDict={}
49        self.paramList=[]
50       
51    def createAssembly(self):
52        """
53        Extract sansmodel and sansdata from self.FitArrangelist ={Uid:FitArrange}
54        Create parkmodel and park data ,form a list couple of parkmodel and parkdata
55        create an assembly self.problem=  park.Assembly([(parkmodel,parkdata)])
56        """
57        mylist=[]
58        listmodel=[]
59        i=0
60        fitproblems=[]
61        for id ,fproblem in self.fitArrangeDict.iteritems():
62            if fproblem.get_to_fit()==1:
63                fitproblems.append(fproblem)
64               
65        if len(fitproblems)==0 : 
66            raise RuntimeError, "No Assembly scheduled for Park fitting."
67            return
68        for item in fitproblems:
69            parkmodel = item.get_model()
70            for p in parkmodel.parameterset:
71                if p._getname()in self.paramList and not p.iscomputed():
72                    p.status = 'fitted' # make it a fitted parameter
73                            #iscomputed  paramter with string inside
74               
75            i+=1
76            Ldata=item.get_data()
77            parkdata=self._concatenateData(Ldata)
78           
79            fitness=(parkmodel,parkdata)
80            mylist.append(fitness)
81   
82        self.problem =  park.Assembly(mylist)
83       
84   
85    def fit(self, qmin=None, qmax=None):
86        """
87            Performs fit with park.fit module.It can  perform fit with one model
88            and a set of data, more than two fit of  one model and sets of data or
89            fit with more than two model associated with their set of data and constraints
90           
91           
92            @param pars: Dictionary of parameter names for the model and their values.
93            @param qmin: The minimum value of data's range to be fit
94            @param qmax: The maximum value of data's range to be fit
95            @note:all parameter are ignored most of the time.Are just there to keep ScipyFit
96            and ParkFit interface the same.
97            @return result.fitness: Value of the goodness of fit metric
98            @return result.pvec: list of parameter with the best value found during fitting
99            @return result.cov: Covariance matrix
100        """
101        self.createAssembly()
102   
103        localfit = FitSimplex()
104        localfit.ftol = 1e-8
105        # fitmc(fitness,localfit,n,handler):
106        #Run a monte carlo fit.
107        #This procedure maps a local optimizer across a set of n initial points.
108        #The initial parameter value defined by the fitness parameters defines
109        #one initial point.  The remainder are randomly generated within the
110        #bounds of the problem.
111        #localfit is the local optimizer to use.  It should be a bounded
112        #optimizer following the `park.fitmc.LocalFit` interface.
113        #handler accepts updates to the current best set of fit parameters.
114        # See `park.fitresult.FitHandler` for details.
115        fitter = FitMC(localfit=localfit)
116        #result = fit.fit(self.problem,
117        #             fitter=fitter,
118        #            handler= GuiUpdate(window))
119        result = fit.fit(self.problem,
120                         fitter=fitter,
121                         handler= fitresult.ConsoleUpdate(improvement_delta=0.1))
122        if result !=None:
123            return result
124        else:
125            raise ValueError, "SVD did not converge"
126           
127       
128       
129   
130   
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